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2020 – today
- 2024
[c9]Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne:
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. AISTATS 2024: 496-504- [i13]Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Saad Hamid, Harald Oberhauser, Michael A. Osborne:
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting. CoRR abs/2404.12219 (2024) - 2023
- [j1]Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne:
Bayesian Quadrature for Neural Ensemble Search. Trans. Mach. Learn. Res. 2023 (2023) - [i12]Masaki Adachi, Satoshi Hayakawa, Saad Hamid, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints. CoRR abs/2301.11832 (2023) - [i11]Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne:
Bayesian Quadrature for Neural Ensemble Search. CoRR abs/2303.08874 (2023) - [i10]Masaki Adachi, Satoshi Hayakawa, Xingchen Wan, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature. CoRR abs/2306.05843 (2023) - 2022
- [c8]Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. AISTATS 2022: 3542-3555 - [c7]Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. NeurIPS 2022 - [c6]Martin Jørgensen, Michael A. Osborne:
Bezier Gaussian Processes for Tall and Wide Data. NeurIPS 2022 - [c5]Pola Schwöbel, Frederik Rahbæk Warburg, Martin Jørgensen, Kristoffer Hougaard Madsen, Søren Hauberg:
Probabilistic spatial transformer networks. UAI 2022: 1749-1759 - [i9]Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. CoRR abs/2206.04734 (2022) - [i8]Martin Jørgensen, Michael A. Osborne:
Bézier Gaussian Processes for Tall and Wide Data. CoRR abs/2209.00343 (2022) - 2021
- [c4]Frederik Warburg, Martin Jørgensen, Javier Civera, Søren Hauberg:
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval. ICCV 2021: 12138-12148 - [c3]Martin Jørgensen, Søren Hauberg:
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. ICML 2021: 5127-5136 - [i7]Pola Elisabeth Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. CoRR abs/2106.07512 (2021) - 2020
- [c2]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. ICML 2020: 4974-4983 - [i6]Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H. Madsen, Søren Hauberg:
Probabilistic Spatial Transformers for Bayesian Data Augmentation. CoRR abs/2004.03637 (2020) - [i5]Martin Jørgensen, Søren Hauberg:
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. CoRR abs/2006.11741 (2020) - [i4]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. CoRR abs/2006.14895 (2020) - [i3]Martin Jørgensen, Søren Hauberg:
Reparametrization Invariance in non-parametric Causal Discovery. CoRR abs/2008.05552 (2020) - [i2]Frederik Warburg, Martin Jørgensen, Javier Civera, Søren Hauberg:
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval. CoRR abs/2011.12663 (2020)
2010 – 2019
- 2019
- [c1]Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg:
Reliable training and estimation of variance networks. NeurIPS 2019: 6323-6333 - [i1]Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg:
Reliable training and estimation of variance networks. CoRR abs/1906.03260 (2019)
Coauthor Index
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